About the Role

The MLOps Engineer enables AI/ML and GenAI solutions to transition reliably from experimentation to enterprise-scale production. In addition to hands-on platform engineering, the role works with AI Engineers, Architects and stakeholders to assess production readiness, recommend deployment approaches and advise on scalability, reliability, security, observability and cost considerations. [MLOps_Engineer | Word]

Key Responsibilities

• Work with AI teams and stakeholders to understand scalability, availability, performance and operational requirements.
• Assess the production readiness of AI/ML and GenAI solutions and provide structured recommendations to close identified gaps.
• Design deployment architectures balancing performance, resilience, security and infrastructure cost.
• Build CI/CD and automated deployment pipelines for AI/ML solutions.
• Implement monitoring, logging, drift detection and observability.
• Establish model versioning, experiment tracking and registry practices.
• Design scalable model-serving infrastructure using Docker and Kubernetes. [MLOps_Engineer | Word]
• Partner with engineering teams to diagnose performance and production issues and develop resolution options.
• Communicate operational risks, dependencies and capacity constraints clearly to both technical teams and delivery stakeholders.
• Recommend optimisation opportunities covering inference performance, infrastructure utilisation, reliability and cost.
• Contribute to production-readiness reviews and advise stakeholders on whether solutions are sufficiently mature for deployment.